Update dataset card

README.md

@@ -146,7 +146,7 @@ Never collected: names, emails, accounts, IP addresses, precise location, cookie
 
 **Browser (`webgpu-1`).** The quick check runs a 256×256 WGSL matrix multiplication for about 1.2 s. The full test runs matrix multiplications at 256, 512 and 1024 plus a 256 MB buffer copy, about 20 s in total. Each phase is normalized to a fixed reference, and the score is the geometric mean × 1000. It measures WebGPU in the browser, **not** MLX or LLM inference.
 
-**MLX (`mlxbench-2`).** `mlx_explorer_bench.py` follows `mlx_lm.benchmark`: it loads the model with `mlx-lm`, builds a random prompt of `prompt_tokens` tokens (seed 0), disables EOS so exactly `generation_tokens` are produced, does one warm-up run, then reports medians over 3 trials, plus peak memory. Chip and RAM class come from `sysctl`.
+**MLX (`mlxbench-2`).** `mlx_explorer_bench.py` follows `mlx_lm.benchmark`: it loads the model with `mlx-lm`, builds a random prompt of `prompt_tokens` tokens (seed 0), disables EOS so exactly `generation_tokens` are produced, does one warm-up run, then reports medians over 3 trials, plus peak memory. If generation speed differs by more than 25% between the fastest and slowest trial (usually memory pressure), speed and TTFT are left empty and only memory and perplexity are reported. Chip and RAM class come from `sysctl`.
 
 **Quality (`--quality`).** The script tokenizes `eval/wikitext-2-raw-v1-test-v1.txt` from this repo: the first ~120k characters of the wikitext-2-raw-v1 test split (Salesforce/wikitext, CC BY-SA 3.0, revision `b08601e`), SHA-256 `5dacf0e5…c730e8`. It scores the first 16,384 tokens in non-overlapping 1,024-token windows and reports `exp(mean cross-entropy)`. If the tokenizer has a BOS token, every window starts with it. It is a coarse quality signal and doesn't measure reasoning or instruction following.